The growing complexity of large infrastructures, such as data centers, frequently hinders the understanding of the system behavior. System administrators frequently analyze metrics extracted from components of the system, relationships between components of the system, as well as the overall system itself.
While data centers have a wide range of sizes from hundreds to thousands of components, it is common to store up to tens or even hundreds different metrics at each timestamp from each component. Depending on the selected period for particular metrics to be read, which is typically a compromise between having the information updated enough and the required resources for the reading, processing, and storing the size of the data to be managed increases exponentially over time. Building management tools that can effectively deal with these volumes of data becomes challenging as the systems grow in complexity. For example, not only is there a need of increased processing power for analyzing the amount of data in a feasible amount of time, but also a need for visualizing a growing volume of data in a limited space and time as system administrators need to react as fast as possible to any anomaly in the system.
Given the sheer size of these infrastructures and the number of metrics to sort through, the challenge of summarizing all this information in a readily understandable description is not practical from a time or resource perspective.